Load Data

# non-imputed data
lfq <- read.csv('./data/nonimputed_lfq.csv')
DT::datatable(lfq)


# imputed data
lfq_imp <- read.csv('./data/LFQ_raw_totals_imp.csv')
colnames(lfq_imp) <- gsub('_TOTALS_', '.I.', colnames(lfq_imp))
DT::datatable(lfq_imp)

Data Preparation

# Extracting only TOTALS data for knockout
LFQ_KO <- lfq %>% select(contains('KO'))

# Extracting only TOTALS data for wild type
LFQ_WT <- lfq %>% select(contains('WT'))
# Extracting only TOTALS data for knockout
LFQ_KO_imp <- lfq_imp %>% select(contains('KO'))

# Extracting only TOTALS data for wild type
LFQ_WT_imp <- lfq_imp %>% select(contains('WT'))

Data Mining

First of all, we have to check the distribution of our data.

Knockout

ggarrange(
    plothist(LFQ_KO, 'Non-Imputed'),
    plothist(LFQ_KO_imp, 'Imputed'),
    nrow = 2, ncol = 1
)

As we see on the historam of konckout columns, the data are slightly skewed to the right.

moments::skewness(cbind(LFQ_KO,LFQ_KO_imp), na.rm=TRUE)
##     KO.22     KO.23     KO.24   KO.I.22   KO.I.23   KO.I.24 
## 0.6754195 0.6725489 0.5218363 0.5761698 0.5868388 0.5206581

The asymmetry score tells use that we need to make some transformation of our data and confirms the conclusions drawn from the histogram.

Wild type

ggarrange(
    plothist(LFQ_WT, 'Non-Imputed'),
    plothist(LFQ_WT_imp, 'Imputed'),
    nrow = 2, ncol = 1
)

As we see on the historam of wild type columns, the data are slightly skewed to the right.

moments::skewness(cbind(LFQ_WT,LFQ_WT_imp), na.rm=TRUE)
##     WT.22     WT.23     WT.24   WT.I.22   WT.I.23   WT.I.24 
## 0.7211065 0.6383786 0.7043256 0.6285968 0.5813767 0.6579983

The asymmetry score tells use that we have to make some transformation of our data and confirms the conclusions drawn from the histogram.

Transformation - KO

Based on the graphic below, we will first use the natural logarithm.

Logarithm

LFQ_KO.log <- log(LFQ_KO) # Non-Imputed
LFQ_KO_imp.log <- log(LFQ_KO_imp) # Imputed

The data distributions looks better, but they are still skewed to the right side.

moments::skewness(cbind(LFQ_KO.log,LFQ_KO_imp.log), na.rm=TRUE)
##     KO.22     KO.23     KO.24   KO.I.22   KO.I.23   KO.I.24 
## 0.4538071 0.4538777 0.2723878 0.3737451 0.3857815 0.3298938

The skewness score is smaler. This is good, but check another transformation.

Fraction \(1/x\)

LFQ_KO.frac <- 1/LFQ_KO # Non-Imputed
LFQ_KO_imp.frac <- 1/LFQ_KO_imp # Imputed

moments::skewness(cbind(LFQ_KO.frac, LFQ_KO_imp.frac), na.rm=TRUE)
##       KO.22       KO.23       KO.24     KO.I.22     KO.I.23     KO.I.24 
## -0.24799683 -0.24994581 -0.02267739 -0.19230177 -0.20508428 -0.15637524

As we see above the plot and scores of the skewness aren’t close to normal distribution. Our result shows us that with simple transformation we can’t transform data and make their distribution closer to the Gaussian.

Box-Cox Transformation

The transformation is based on the formula:

\[x' = \Bigg\{ \matrix{\frac{x^{\lambda}-1}{\lambda} & \lambda \neq0 \\ log(x) & \lambda = 0}\]

### Non-Imputed ###
# At first we have to calculate the lambda
lambda.22 <- car::powerTransform(LFQ_KO$KO.22)$lambda
lambda.23 <- car::powerTransform(LFQ_KO$KO.23)$lambda
lambda.24 <- car::powerTransform(LFQ_KO$KO.24)$lambda


The lambda is not 0, so the first transformation is performed.

LFQ_KO.boxcox.22 <- car::bcPower(LFQ_KO$KO.22, lambda.22)
LFQ_KO.boxcox.23 <- car::bcPower(LFQ_KO$KO.23, lambda.23)
LFQ_KO.boxcox.24 <- car::bcPower(LFQ_KO$KO.24, lambda.24)
LFQ_KO.boxcox <- data.frame(LFQ_KO.boxcox.22, LFQ_KO.boxcox.23, LFQ_KO.boxcox.24)
colnames(LFQ_KO.boxcox) <- c('KO.22', 'KO.23', 'KO.24')
### Imputed ###
# At first we have to calculate the lambda
lambda.22 <- car::powerTransform(LFQ_KO_imp$KO.I.22)$lambda
lambda.23 <- car::powerTransform(LFQ_KO_imp$KO.I.23)$lambda
lambda.24 <- car::powerTransform(LFQ_KO_imp$KO.I.24)$lambda


The lambda is not 0, so the first transformation is performed.

LFQ_KO.boxcox.22 <- car::bcPower(LFQ_KO_imp$KO.I.22, lambda.22)
LFQ_KO.boxcox.23 <- car::bcPower(LFQ_KO_imp$KO.I.23, lambda.23)
LFQ_KO.boxcox.24 <- car::bcPower(LFQ_KO_imp$KO.I.24, lambda.24)
LFQ_KO_imp.boxcox <- data.frame(LFQ_KO.boxcox.22, LFQ_KO.boxcox.23, LFQ_KO.boxcox.24)
colnames(LFQ_KO_imp.boxcox) <- c('KO.I.22', 'KO.I.23', 'KO.I.24')

moments::skewness(cbind(LFQ_KO.boxcox, LFQ_KO_imp.boxcox), na.rm=TRUE)
##       KO.22       KO.23       KO.24     KO.I.22     KO.I.23     KO.I.24 
##  0.01691875  0.01817077 -0.01003888  0.04771938  0.05134458  0.03856700

Finally we have got expected results. The distributions are similar to a normal distribution. 🎉

Normalization - KO

Z-score Normalization

Z-score normalization standardizes data by subtracting the mean and dividing by the standard deviation. This technique transforms data into a distribution with a mean of 0 and a standard deviation of 1.

Formula: \(\tilde{y}_{ij} = \frac{y_ij - \bar{y_j}}{\theta_j}\), where:

  • \(y_ij\) - value of the LFQ;
  • \(\bar{y_j}\) - mean of the LFQ values;
  • \(\theta_j\) - standard deviation of the LFQ values.
LFQ_KO.standard <- as.data.frame(scale(LFQ_KO)) # Non-Imputed
LFQ_KO_imp.standard <- as.data.frame(scale(LFQ_KO_imp)) # Imputed

moments::skewness(cbind(LFQ_KO.standard, LFQ_KO_imp.standard), na.rm=TRUE)
##     KO.22     KO.23     KO.24   KO.I.22   KO.I.23   KO.I.24 
## 0.6754195 0.6725489 0.5218363 0.5761698 0.5868388 0.5206581

Unfortunately the standardization didn’t help to bring the distribution closer to the normal dist.

Min-max Normalization

Min-max normalization scales data to a specified range (usually [0, 1]) by subtracting the minimum value and dividing by the range of values.

min_max_norm <- function(df) {
    df_no_na <- na.omit(df)
    ret <- scale(df_no_na, center = min(df_no_na), scale = max(df_no_na) - min(df_no_na))
    df[which(!is.na(df))] <- ret
    return(df)
}

LFQ_KO.minmax <- as.data.frame(lapply(LFQ_KO, function(col) min_max_norm(col)))  # Non-Imputed
LFQ_KO_imp.minmax <- as.data.frame(lapply(LFQ_KO_imp, function(col) min_max_norm(col)))  # Imputed

moments::skewness(cbind(LFQ_KO.minmax, LFQ_KO_imp.minmax), na.rm=TRUE)
##     KO.22     KO.23     KO.24   KO.I.22   KO.I.23   KO.I.24 
## 0.6754195 0.6725489 0.5218363 0.5761698 0.5868388 0.5206581

Unfortunately, again the normalization didn’t help. 😒

Median Scaling

LFQ_KO.med <- as.data.frame(DescTools::RobScale(LFQ_KO, scale=FALSE)) # Non-Imputed
LFQ_KO_imp.med <- as.data.frame(DescTools::RobScale(LFQ_KO_imp, scale=FALSE)) # Imputed

moments::skewness(cbind(LFQ_KO.med,LFQ_KO_imp.med), na.rm=TRUE)
##     KO.22     KO.23     KO.24   KO.I.22   KO.I.23   KO.I.24 
## 0.6754195 0.6725489 0.5218363 0.5761698 0.5868388 0.5206581

MAD Scaling

LFQ_KO.mad <- as.data.frame(DescTools::RobScale(LFQ_KO)) # Non-Imputed
LFQ_KO_imp.mad <- as.data.frame(DescTools::RobScale(LFQ_KO_imp)) # Imputed

moments::skewness(cbind(LFQ_KO.mad,LFQ_KO_imp.mad), na.rm=TRUE)
##     KO.22     KO.23     KO.24   KO.I.22   KO.I.23   KO.I.24 
## 0.6754195 0.6725489 0.5218363 0.5761698 0.5868388 0.5206581

Bove Median and Mad Scaling didn’t give us the expected result.

Linear Regression Normalization

moments::skewness(cbind(LFQ_KO.lm, LFQ_KO_imp.lm), na.rm=TRUE)
##     KO.22     KO.23     KO.24   KO.I.22   KO.I.23   KO.I.24 
## 0.6754195 0.6725489 0.5218363 0.5761698 0.5868388 0.5206581

Linear Regression also didn’t give us expected result.

Transformation - WT

Like earlier we will first use the logarithmic transformation.

Logarithm

LFQ_WT.log <- log(LFQ_WT) # Non-Imputed
LFQ_WT_imp.log <- log(LFQ_WT_imp) # Imputed

moments::skewness(cbind(LFQ_WT.log, LFQ_WT_imp.log), na.rm=TRUE)
##     WT.22     WT.23     WT.24   WT.I.22   WT.I.23   WT.I.24 
## 0.5066689 0.4177543 0.4800066 0.4279311 0.3823422 0.4567911

Unfortunantely the result isn’t closer to the Gaussian distribution.

Fraction \(1/x\)

LFQ_WT.frac <- 1/LFQ_WT # Non-Imputed
LFQ_WT_imp.frac <- 1/LFQ_WT_imp # Imputed

As we can see above, the results are the same like for the knockout cell type. With simple methods we can’t make the distribution closer to Gaussian dist.

Box-Cox Transformation

The transformation is based on the formula:

\[x' = \Bigg\{ \matrix{\frac{x^{\lambda}-1}{\lambda} & \lambda \neq0 \\ log(x) & \lambda = 0}\]

### Non-Imputed ###
# At first we have to calculate the lambda
lambda.22 <- car::powerTransform(LFQ_WT$WT.22)$lambda
lambda.23 <- car::powerTransform(LFQ_WT$WT.23)$lambda
lambda.24 <- car::powerTransform(LFQ_WT$WT.24)$lambda


The lambda is not 0, so the first transformation is performed.

LFQ_WT.boxcox.22 <- car::bcPower(LFQ_WT$WT.22, lambda.22)
LFQ_WT.boxcox.23 <- car::bcPower(LFQ_WT$WT.23, lambda.23)
LFQ_WT.boxcox.24 <- car::bcPower(LFQ_WT$WT.24, lambda.24)
LFQ_WT.boxcox <- data.frame(LFQ_WT.boxcox.22, LFQ_WT.boxcox.23, LFQ_WT.boxcox.24)
colnames(LFQ_WT.boxcox) <- c('WT.22', 'WT.23', 'WT.24')

Let’s check the imputed data.

### Imputed ###
# At first we have to calculate the lambda
lambda.22 <- car::powerTransform(LFQ_WT_imp$WT.I.22)$lambda
lambda.23 <- car::powerTransform(LFQ_WT_imp$WT.I.23)$lambda
lambda.24 <- car::powerTransform(LFQ_WT_imp$WT.I.24)$lambda


The lambda is not 0, so the first transformation is performed.

LFQ_WT.boxcox.22 <- car::bcPower(LFQ_WT_imp$WT.I.22, lambda.22)
LFQ_WT.boxcox.23 <- car::bcPower(LFQ_WT_imp$WT.I.23, lambda.23)
LFQ_WT.boxcox.24 <- car::bcPower(LFQ_WT_imp$WT.I.24, lambda.24)
LFQ_WT_imp.boxcox <- data.frame(LFQ_WT.boxcox.22, LFQ_WT.boxcox.23, LFQ_WT.boxcox.24)
colnames(LFQ_WT_imp.boxcox) <- c('WT.I.22', 'WT.I.23', 'WT.I.24')

moments::skewness(cbind(LFQ_WT.boxcox, LFQ_WT_imp.boxcox), na.rm=TRUE)
##      WT.22      WT.23      WT.24    WT.I.22    WT.I.23    WT.I.24 
## 0.02095747 0.01822482 0.02082671 0.05750385 0.05404352 0.06835686

And again the Box-Cox Tranformation gave us expected result. The distributions are similar to a normal distribution.

Normalization - WT

Z-score Normalization

LFQ_WT.standard <- as.data.frame(scale(LFQ_WT)) # Non-Imputed
LFQ_WT_imp.standard <- as.data.frame(scale(LFQ_WT_imp)) # Imputed

moments::skewness(cbind(LFQ_WT.standard, LFQ_WT_imp.standard), na.rm=TRUE)
##     WT.22     WT.23     WT.24   WT.I.22   WT.I.23   WT.I.24 
## 0.7211065 0.6383786 0.7043256 0.6285968 0.5813767 0.6579983

Unfortunately the standardization didn’t help to bring the distribution closer to the normal dist.

Min-max Normalization

LFQ_WT.minmax <- as.data.frame(lapply(LFQ_WT, function(col) min_max_norm(col))) # Non-Imputed
LFQ_WT_imp.minmax <- as.data.frame(lapply(LFQ_WT_imp, function(col) min_max_norm(col))) # Imputed

moments::skewness(cbind(LFQ_WT.minmax, LFQ_WT_imp.minmax), na.rm=TRUE)
##     WT.22     WT.23     WT.24   WT.I.22   WT.I.23   WT.I.24 
## 0.7211065 0.6383786 0.7043256 0.6285968 0.5813767 0.6579983

Unfortunately, again the normalization didn’t help. 😒

Median Scaling

LFQ_WT.med <- as.data.frame(DescTools::RobScale(LFQ_WT, scale = FALSE)) # Non-Imputed
LFQ_WT_imp.med <- as.data.frame(DescTools::RobScale(LFQ_WT_imp, scale = FALSE)) # Imputed

moments::skewness(cbind(LFQ_WT.med,LFQ_WT_imp.med), na.rm=TRUE)
##     WT.22     WT.23     WT.24   WT.I.22   WT.I.23   WT.I.24 
## 0.7211065 0.6383786 0.7043256 0.6285968 0.5813767 0.6579983

MAD Scaling

LFQ_WT.mad <- as.data.frame(DescTools::RobScale(LFQ_WT)) # Non-Imputed
LFQ_WT_imp.mad <- as.data.frame(DescTools::RobScale(LFQ_WT_imp)) # Imputed

moments::skewness(cbind(LFQ_WT.mad,LFQ_WT_imp.mad), na.rm=TRUE)
##     WT.22     WT.23     WT.24   WT.I.22   WT.I.23   WT.I.24 
## 0.7211065 0.6383786 0.7043256 0.6285968 0.5813767 0.6579983

Linear Regression Normalization

moments::skewness(cbind(LFQ_WT.lm, LFQ_WT_imp.lm), na.rm=TRUE)
##     WT.22     WT.23     WT.24   WT.I.22   WT.I.23   WT.I.24 
## 0.7211065 0.6383786 0.7043256 0.6285968 0.5813767 0.6579983

VSN

VSN - Variance Stabilization Normalization

lfq_rglist<- new('RGList', list(
    R = as.matrix(LFQ_KO),
    G = as.matrix(LFQ_WT)))

lfq_rglist_imp<- new('RGList', list(
    R = as.matrix(LFQ_KO_imp),
    G = as.matrix(LFQ_WT_imp)))

LFQ.vsn <- justvsn(lfq_rglist)@assayData  # Non-Imputed
LFQ_imp.vsn <- justvsn(lfq_rglist_imp)@assayData # Imputed

This transformation didn’t help us too. Furthermore we lost differences between knockout and wild type intensity.

# Non-imputed
moments::skewness(cbind(LFQ.vsn$R, LFQ.vsn$G), na.rm=TRUE)
##     KO.22     KO.23     KO.24     WT.22     WT.23     WT.24 
## 0.4488821 0.4558896 0.2747223 0.5049867 0.4228609 0.4813897
# Imputed
moments::skewness(cbind(LFQ_imp.vsn$R, LFQ_imp.vsn$G), na.rm=TRUE)
##   KO.I.22   KO.I.23   KO.I.24   WT.I.22   WT.I.23   WT.I.24 
## 0.5754457 0.5861375 0.5199840 0.6278960 0.5806866 0.6572911

bestNormalize Package for Automatic Normalization

KO

Experiment 22

best.KO.22 <- bestNormalize::bestNormalize(LFQ_KO$KO.22) # Non-Imputed
best.KO_imp.22 <- bestNormalize::bestNormalize(LFQ_KO_imp$KO.I.22) # Imputed

Experiment 23

best.KO.23 <- bestNormalize::bestNormalize(LFQ_KO$KO.23) # Non-Imputed
best.KO_imp.23 <- bestNormalize::bestNormalize(LFQ_KO_imp$KO.I.23) # Imputed

Experiment 24

best.KO.24 <- bestNormalize::bestNormalize(LFQ_KO$KO.24) # Non-Imputed
best.KO_imp.24 <- bestNormalize::bestNormalize(LFQ_KO_imp$KO.I.24) # Imputed

Summary

##           Rep_22 Rep_22_imp    Rep_23 Rep_23_imp     Rep_24 Rep_24_imp
## Method orderNorm  orderNorm orderNorm  orderNorm yeojohnson  orderNorm

Based on the result for each column, the best method to normalize the data is Order Quntile Normalization for all of the samples:

moments::skewness(cbind(LFQ_KO.bestNorm, LFQ_KO_imp.bestNorm), na.rm = TRUE)
##              KO.22              KO.23              KO.24            KO.I.22 
##  0.000000065283662  0.000000002353212 -1.087327117083625 -0.000000019535102 
##            KO.I.23            KO.I.24 
## -0.000000004631138 -0.000000021849734

WT

Experiment 22

best.WT.22 <- bestNormalize::bestNormalize(LFQ_WT$WT.22) # Non-Imputed
best.WT_imp.22 <- bestNormalize::bestNormalize(LFQ_WT_imp$WT.I.22) # Imputed

Experiment 23

best.WT.23 <- bestNormalize::bestNormalize(LFQ_WT$WT.23) # Non-Imputed
best.WT_imp.23 <- bestNormalize::bestNormalize(LFQ_WT_imp$WT.I.23) # Imputed

Experiment 24

best.WT.24 <- bestNormalize::bestNormalize(LFQ_WT$WT.24) # Non-Imputed
best.WT_imp.24 <- bestNormalize::bestNormalize(LFQ_WT_imp$WT.I.24) # Imputed

Summary

##           Rep_22 Rep_22_imp     Rep_23 Rep_23_imp    Rep_24 Rep_24_imp
## Method orderNorm  orderNorm yeojohnson  orderNorm orderNorm  orderNorm

Like earlier the best method for each column is Order Quantile Normalization.

moments::skewness(cbind(LFQ_WT.bestNorm, LFQ_WT_imp.bestNorm), na.rm = TRUE)
##              WT.22              WT.23              WT.24            WT.I.22 
##  0.000000255971820 -0.506896422649866  0.000000084736746 -0.000000003968973 
##            WT.I.23            WT.I.24 
## -0.000000045719609 -0.000000016718898

As we can see both KO and WT data have a normal distribution, but we have lost the differences between samples.

EigenMS Non-Imputed

protein.groups <- readr::read_tsv('data/proteinGroups.txt', show_col_types = FALSE)
protein.groups <- protein.groups %>% filter(is.na(`Only identified by site`),
                         is.na(Reverse),
                         is.na(`Potential contaminant`))

# I needed unique values for each peptide, so I create artificial names `prot_1:5905`
prot.info <- data.frame(protein.groups[,"Peptide sequences"], paste('prot_', 1:nrow(lfq), sep = ''))
LFQ.eig1 <- eig_norm1(lfq, treatment = as.factor(c('KO', 'KO', 'KO', 'WT', 'WT', 'WT')), prot.info = prot.info)

On the plots we can see the result of svd() fxn, which is:

The SVD decomposition of the matrix as computed by LAPACK,

\[X=UDV',\]

where \(U\) and \(V\) are orthogonal, \(V'\) means V transposed (and conjugated for complex input), and \(D\) is a diagonal matrix with the (non-negative) singular values \(D_{ii}\) in decreasing order. Equivalently, \(D=U'XV\), which is verified in the examples.

The returned value is a list with components:

  • d - a vector containing the singular values of x, of length min(n, p), sorted decreasingly.
  • u - a matrix whose columns contain the left singular vectors of x, present if nu > 0. Dimension c(n, nu).
  • v - a matrix whose columns contain the right singular vectors of x, present if nv > 0. Dimension c(p, nv).

Axis of the plots:

  • x-axis - the number corresponding to the imputed samples in the order in which we imputed them.
  • y-axis - the u value from the svd() fnx.
# Performing eig normalization
LFQ.eig_norm <- eig_norm2(LFQ.eig1)

par(mfcol=c(1,2))
boxplot(lfq, las=2, main='Raw intensities')
boxplot(LFQ.eig_norm$norm_m, las=2, main='Normalized intensities')

ggarrange(
    plothist(LFQ.eig_norm$norm_m[,1:3], 'Non-Imputed EigenMS'),
    plothist(LFQ.eig_norm$norm_m[,4:6], ''),
    nrow=2,ncol=1
)

moments::skewness(LFQ.eig_norm$norm_m, na.rm=TRUE)
##     KO.22     KO.23     KO.24     WT.22     WT.23     WT.24 
## 0.7008182 0.7392194 0.7785731 0.7016820 0.6860371 0.7237159

The eigen normalization perfectly shows the differences between cell type, but distributions are skewed all the time.

EigenMS Imputed

# I needed unique values for each peptide, so I create artificial names `prot_1:5905`
prot.info <- data.frame(protein.groups[,"Peptide sequences"], paste('prot_', 1:nrow(lfq_imp), sep = ''))
LFQ_imp.eig1 <- eig_norm1(lfq_imp, treatment = as.factor(c('KO', 'KO', 'KO', 'WT', 'WT', 'WT')), prot.info = prot.info)
## The following object is masked from TREAT (pos = 3):
## 
##     TREAT

# Performing eig normalization
LFQ_imp.eig_norm <- eig_norm2(LFQ_imp.eig1)

par(mfcol=c(1,2))
boxplot(lfq_imp, las=2, main='Raw intensities')
boxplot(LFQ_imp.eig_norm$norm_m, las=2, main='Normalized intensities')

ggarrange(
    plothist(LFQ_imp.eig_norm$norm_m[,1:3], 'Imputed EigenMS'),
    plothist(LFQ_imp.eig_norm$norm_m[,4:6], ''),
    nrow=2,ncol=1
)

moments::skewness(LFQ_imp.eig_norm$norm_m, na.rm=TRUE)
##   KO.I.22   KO.I.23   KO.I.24   WT.I.22   WT.I.23   WT.I.24 
## 0.6040135 0.6322023 0.6980207 0.6800863 0.6629387 0.6618507

Comparison

Visualizations

Non-Imputed

TRANSFORMATIONS

NORMALIZATIONS

HISTOGRAMS

Imputed

TRANSFORMATIONS

NORMALIZATIONS

HISTOGRAMS

Statistical Metrics

COEFFICIENT OF VARIATION (CV) / RELATIVE STANDARD DEVIATION (RSD)

This is a way to measure how spread out values are in a dataset relative to the mean. A lower RSD/CV indicates better normalization. It is calculated as:

\(CV = \frac{\sigma}{\mu}\)

where:

  • \(\sigma\): The standard deviation of dataset
  • \(\mu\): The mean of dataset

Non-Imputed CV

SUMMARY OF COEFFICIENT OF VARIATION


As we can see above, the best methods were:

  • Min-Max Normalization
  • VSN
  • EigenMS

Coefficient of Variation for the rest of the methods have a poor performance.

Imputed CV

SUMMARY OF COEFFICIENT OF VARIATION


REPRODUCIBILITY OF BIOLOGICAL REPLICATES

After normalization, biological replicates should group more tightly. You can assess this by measuring the intraclass correlation coefficient (ICC) to see if replicates cluster together.

A guidelines for interpretation by Koo and Li (2016):

  • below 0.50: poor ■
  • between 0.50 and 0.75: moderate ■
  • between 0.75 and 0.90: good ■
  • above 0.90: excellent ■

Non-Imputed ICC

Imputed ICC

Conclusion

All normalization methods worked quite well. The skewness problem was solved with the Box-Cox transformation, and with transformed data we can normalize it with all methods.

The method chosen with bestNormalize gave us a perfect shape of a distribution, but we lost the main differences between samples. In my opinion the best result gave the Eigen Normalization method, because this method kept a characteristic of a sample and this is very important in the analysis of biological data.